Achieving a sum-rate that is close to optimal is not enough as the time required for power allocation has to be synchronized with fading conditions that are fast evolving. It is therefore essential to consider the time taken by each algorithm to offer a power allocation output given a channel state input.
To that end, we provide a computation time comparisonAll computations were performed on an Nvidia Quadro T2000 GPU. among the various algorithms in Table REF . UWMMSE, which takes close to 2 ms per sample, is significantly faster as compared to WMMSE which takes around 16 ms per sample.
All the other learning-based methods  [1]}, [2]}, [3]} have a processing time similar to that of UWMMSE, however, none of them achieve the same performance in that time duration, which is the main advantage of our method over existing algorithms. Unlike WMMSE and similar to any other learning-based method, UWMMSE has the additional requirement of training, which takes close to 15 mins, but in most applications this is not a problem since we envision the computational effort to be done prior to implementation. MLP [2]} has significantly lower training time, but requires a full iteration of WMMSE over its training data to get the ground-truth power allocation which adds to the time complexity.
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